Oceans.ai’s systems and solutions boost the Industry 4.0 innovation processes, Some use-case scenarios: 

• Plants are shut down for 1-2 months per year to enable planned inspections and maintenance, creating downtime. 
• If this downtime can be reduced by even 10-20%, it will make a pivotal difference 
• Efficient asset inspections, including remote assets, are preventative measures that will significantly reduce downtime, operational and profit losses.  
• In toxic environments, instead of shutdown and wait periods (to remove toxicity), plants can use robots to perform regular plant inspections remotely. 

Intelligent Robotic Remote Plant Inspections (Scheduled and Unscheduled)

• This, along with IoT sensors, plant gauges and control system data, will give remote operations and AI-powered systems sufficient intelligence to make decisions related to shutdown inspection.
• Stray mixing of raw materials (including water) in chemical plants across units can be catastrophic. By combining visual, thermal, and sensor-based data, our solutions can prevent or reduce risk.

Optimised Plant Monitoring

• This is achieved through a mix of in-asset sensors, context-aware on-robot sensors, plant gauge readings and control system data. 
• Today manufacturing is powered by various systems and processes, both connected and disconnected.
• Seamless integration between systems and processes by making meaningful interpretations and corroborations with available data 
• Having all IoT sensors across plant assets can be costly for monitoring – this is where our solution, which combines on-asset sensors with on-robot sensors, can optimise sensor deployment across the plant.

Plant Simulation and Predictive Planning 

• Sensors attached to plant assets, or through regular robotic inspections, will transmit data to the digital twin, providing the manufacturer with critical information about the plants’ performance. This information can be used to conduct simulations and predict issues. 

Safer Environments  

• Work safety and compliance and manufacturing environment monitoring can significantly reduce human costs and human-led error in manufacturing environments. 
• Oceans.ai enables manufacturers to monitor worker activity, machinery compliance, and causes of safety shutdowns in real time. We use industrial IoT data among other datasets. Employees can wear smart devices to track biometrics like body temperature, allowing supervisors to oversee health concerns. 
• Finally, machine learning can enable video and robotic surveillance systems to detect when workers are not wearing proper protective equipment or engaged in unsafe practices. Paired with sensors on the shop floor and robotic inspection data, machine learning can also provide insight into the performance of safety systems to identify root causes of shutdowns and identify leading indicators of machinery issues. 

Predictive Maintenance

Predictive Maintenance (PdM) leverages real-time data to promptly identify core issues in the manufacturing process and take necessary corrective actions. It helps to analyse data by studying the difference in nature and frequency and alerts the system to reduce the potential risks of failure. PdM guides the manufacturing industries in optimising top-notch quality products at a lower cost. 

PdM systems can also help companies predict what replacement parts will be needed. Using machine learning for predictive maintenance allows manufacturers to foresee when equipment failure is likely, to enable proactive part replacement or repairs.

Manufacturing: Chemical Plants, Petrochemical Plants, Offshore and Onshore refineries 

• Automated quality testing can be realised using AI. By employing advanced image recognition techniques for visual inspection and fault detection, productivity increases of up to 50% are possible.
• Collaborative robots are particularly relevant concerning tasks that are not fully automatable. In such settings, they hold the potential of increasing productivity by up to 20%.
• “ Inspection costs may be reduced by up to 25%, and an overall reduction of up to 10% of annual maintenance costs is possible. “ 

Source: smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector?